mock-data
Creates typed mock data files in src/data/ following project conventions. Use when needing test data for new features or components.
Best use case
mock-data is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Creates typed mock data files in src/data/ following project conventions. Use when needing test data for new features or components.
Teams using mock-data should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/mock-data/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How mock-data Compares
| Feature / Agent | mock-data | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Creates typed mock data files in src/data/ following project conventions. Use when needing test data for new features or components.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# Mock Data Generator Skill
Creates typed mock data files following project patterns.
## File Pattern
```text
src/data/mock-{feature}.ts
```
## Template Structure
```typescript
// src/data/mock-{feature}.ts
export interface {Feature}Data {
id: string;
name: string;
// Add fields based on requirements
}
export const mock{Feature}Data: {Feature}Data = {
id: "1",
name: "Example",
};
// For arrays:
export const mock{Feature}List: {Feature}Data[] = [
{ id: "1", name: "Item 1" },
{ id: "2", name: "Item 2" },
];
```
## Conventions
1. **Filename**: kebab-case with `mock-` prefix
2. **Named exports**: Both interface and data
3. **Type-first**: Define interface before data
4. **Realistic data**: Use meaningful values, not "test123"
## Examples
See these files for reference patterns:
- `src/data/mock-order.ts`
- `src/data/mock-project.ts`
- `src/data/mock-unmatched-items.ts`
## Usage with TanStack Query
```typescript
// hooks/use{Feature}.ts
import { useQuery } from "@tanstack/react-query";
import { mock{Feature}Data, {Feature}Data } from "../data/mock-{feature}";
async function fetch{Feature}Data(): Promise<{Feature}Data> {
await new Promise((r) => setTimeout(r, 500)); // Simulate API
return mock{Feature}Data;
}
export function use{Feature}() {
return useQuery({
queryKey: ["{feature}"],
queryFn: fetch{Feature}Data,
});
}
```Related Skills
parallel-data-enrichment
Structured company and entity data enrichment using Parallel AI Task API with core/base processors. Returns typed JSON output. No binary install — requires PARALLEL_API_KEY in .env.local.
pandas-data-manipulation-rules
Focuses on pandas-specific rules for data manipulation, including method chaining, data selection using loc/iloc, and groupby operations.
native-data-fetching
Use when implementing or debugging ANY network request, API call, or data fetching. Covers fetch API, axios, React Query, SWR, error handling, caching strategies, offline support.
large-data-with-dask
Specific optimization strategies for Python scripts working with larger-than-memory datasets via Dask.
ipdata-co-automation
Automate Ipdata co tasks via Rube MCP (Composio). Always search tools first for current schemas.
gdpr-data-handling
Implement GDPR-compliant data handling with consent management, data subject rights, and privacy by design. Use when building systems that process EU personal data, implementing privacy controls, o...
fair-data-model-assessment
Assess data models against FAIR principles using RDA-FDMM indicators. Use when: (1) Evaluating vendor-delivered data models for FAIR compliance, (2) Reviewing schemas, ontologies, or data dictionaries before integration, (3) Creating FAIR assessment reports for data governance reviews, (4) Preparing data model documentation for enterprise or regulatory standards, (5) Auditing existing data assets for FAIRness gaps. Covers 41 RDA indicators across Findable, Accessible, Interoperable, Reusable dimensions with maturity scoring (0-4 scale).
docker-database
Configure database containers with security, persistence, and health checks
datarobot-automation
Automate Datarobot tasks via Rube MCP (Composio). Always search tools first for current schemas.
dataql-analysis
Analyze data files using SQL queries with DataQL. Use when working with CSV, JSON, Parquet, Excel files or when the user mentions data analysis, filtering, aggregation, or SQL queries on files.
datahub-connector-pr-review
This skill should be used when the user asks to "review my connector", "check my datahub connector", "review connector code", "audit connector", "review PR", "check code quality", or any request to review/check/audit a DataHub ingestion source. Covers compliance with standards, best practices, testing quality, and merge readiness.
datagma-automation
Automate Datagma tasks via Rube MCP (Composio). Always search tools first for current schemas.